statistical parametric mapping spm matlab toolbox (MathWorks Inc)
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Statistical Parametric Mapping Spm Matlab Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 670 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 96 stars, based on 670 article reviews
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1) Product Images from "Investigating the neurovascular coupling across multiple motor execution and imagery conditions: a whole-brain EEG-informed fMRI analysis"
Article Title: Investigating the neurovascular coupling across multiple motor execution and imagery conditions: a whole-brain EEG-informed fMRI analysis
Journal: NeuroImage
doi: 10.1016/j.neuroimage.2025.121311
Figure Legend Snippet: A: Group-averaged t values of the contrast right-hand ME > left-hand ME ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 17). B: Group-averaged t values of the contrast left-hand ME > right-hand ME ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 17). C: Group-averaged t values of the contrast right-hand MI > left-hand MI ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 17). D: Group-averaged t values of the contrast left-hand MI > right-hand MI ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 17). Results are displayed using the BrainNet toolbox in MATLAB.
Techniques Used:
Figure Legend Snippet: A: Group-averaged t values showing BOLD correlates of alpha rhythms in channel C3 during ME tasks ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 10). B: group-averaged t values showing neural correlates of alpha rhythms in channel C4 during ME tasks ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 10). C: Group-averaged t values showing neural correlates of alpha rhythms in channel C3 during MI tasks ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 10). Results are displayed using the BrainNet toolbox in MATLAB.
Techniques Used:
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